The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
Important assumptions in regression model analysis are
- There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
- There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
- The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
- The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
- The error terms must be normally distributed.
Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
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I would help but Can u add a pic or something for more understanding
You didnt list any graph?? Can you add it please :)
Answer:
5.25 times
Step-by-step explanation:
Beverage A and Beverage B are sold in identical cans.
From the above question
Beverage A is 4% sugar.
The portion of Beverage B that is sugar is 0.21.
Converting Portion of sugar in Beverage B to percentage we have :
0.21 × 100
= 21%
How many times more sugar is in Beverage B than Beverage A?
This is calculated as:
% Beverage B/% Beverage A
= 21%/4%
= 5.25
Hence Beverage B has 5.25 times more sugar than Beverage A.
We need to cross multiply
So we start off by multiplying the numerator of the right fraction by the denominator of the left fraction, and the same thing only switched around. We get the equation:
3n=24
divide both sides by 3 to get
n=8
